Supplementary Material for: The relationship between age and cognitive subtypes of Alzheimer’s disease and related dementias
Bibliographic record
Abstract
Introduction: We aimed to identify cognitive subgroups of clinically diagnosed Alzheimer’s disease (Alzheimer syndrome) and related dementias and test if age of presentation influences patterns of cognitive impairment. Methods: Participants were individuals with mild cognitive impairment (n=360), vascular mild cognitive impairment (n=73), Alzheimer syndrome (n=127), vascular dementia (n=32), mixed dementia (n=23), and healthy controls (n=305). Principal Component analysis was run on 25 cognitive variables measured by the Toronto Cognitive Assessment (TorCA). We used hierarchical clustering to identify cognitive subgroups. Results: We identified seven subgroups. The youngest group was characterized by the lowest scores on the overall TorCA mean, and also on Executive Function, Visuospatial function and Attention, while showing the highest scores on Memory, Orientation, and Language. The oldest clusters had low scores on Memory and Orientation, but higher scores on Attention. The intermediary clusters had similar age and severity distributions. Conclusion: The results demonstrate that patterns of cognitive impairment are different in different age groups.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.157 | 0.059 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".